| import os |
| import numpy as np |
| import torch |
| import torch.nn as nn |
| from functools import partial |
| from lib.model.DSTformer import DSTformer |
|
|
| class AverageMeter(object): |
| """Computes and stores the average and current value""" |
| def __init__(self): |
| self.reset() |
|
|
| def reset(self): |
| self.val = 0 |
| self.avg = 0 |
| self.sum = 0 |
| self.count = 0 |
|
|
| def update(self, val, n=1): |
| self.val = val |
| self.sum += val * n |
| self.count += n |
| self.avg = self.sum / self.count |
| |
| def accuracy(output, target, topk=(1,)): |
| """Computes the accuracy over the k top predictions for the specified values of k""" |
| with torch.no_grad(): |
| maxk = max(topk) |
| batch_size = target.size(0) |
| _, pred = output.topk(maxk, 1, True, True) |
| pred = pred.t() |
| correct = pred.eq(target.view(1, -1).expand_as(pred)) |
| res = [] |
| for k in topk: |
| correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True) |
| res.append(correct_k.mul_(100.0 / batch_size)) |
| return res |
|
|
| def load_pretrained_weights(model, checkpoint): |
| """Load pretrianed weights to model |
| Incompatible layers (unmatched in name or size) will be ignored |
| Args: |
| - model (nn.Module): network model, which must not be nn.DataParallel |
| - weight_path (str): path to pretrained weights |
| """ |
| import collections |
| if 'state_dict' in checkpoint: |
| state_dict = checkpoint['state_dict'] |
| else: |
| state_dict = checkpoint |
| model_dict = model.state_dict() |
| new_state_dict = collections.OrderedDict() |
| matched_layers, discarded_layers = [], [] |
| for k, v in state_dict.items(): |
| |
| |
| if k.startswith('module.'): |
| k = k[7:] |
| if k in model_dict and model_dict[k].size() == v.size(): |
| new_state_dict[k] = v |
| matched_layers.append(k) |
| else: |
| discarded_layers.append(k) |
| model_dict.update(new_state_dict) |
| model.load_state_dict(model_dict, strict=True) |
| print('load_weight', len(matched_layers)) |
| return model |
|
|
| def partial_train_layers(model, partial_list): |
| """Train partial layers of a given model.""" |
| for name, p in model.named_parameters(): |
| p.requires_grad = False |
| for trainable in partial_list: |
| if trainable in name: |
| p.requires_grad = True |
| break |
| return model |
|
|
| def load_backbone(args): |
| if not(hasattr(args, "backbone")): |
| args.backbone = 'DSTformer' |
| if args.backbone=='DSTformer': |
| model_backbone = DSTformer(dim_in=3, dim_out=3, dim_feat=args.dim_feat, dim_rep=args.dim_rep, |
| depth=args.depth, num_heads=args.num_heads, mlp_ratio=args.mlp_ratio, norm_layer=partial(nn.LayerNorm, eps=1e-6), |
| maxlen=args.maxlen, num_joints=args.num_joints) |
| elif args.backbone=='TCN': |
| from lib.model.model_tcn import PoseTCN |
| model_backbone = PoseTCN() |
| elif args.backbone=='poseformer': |
| from lib.model.model_poseformer import PoseTransformer |
| model_backbone = PoseTransformer(num_frame=args.maxlen, num_joints=args.num_joints, in_chans=3, embed_dim_ratio=32, depth=4, |
| num_heads=8, mlp_ratio=2., qkv_bias=True, qk_scale=None,drop_path_rate=0, attn_mask=None) |
| elif args.backbone=='mixste': |
| from lib.model.model_mixste import MixSTE2 |
| model_backbone = MixSTE2(num_frame=args.maxlen, num_joints=args.num_joints, in_chans=3, embed_dim_ratio=512, depth=8, |
| num_heads=8, mlp_ratio=2., qkv_bias=True, qk_scale=None,drop_path_rate=0) |
| elif args.backbone=='stgcn': |
| from lib.model.model_stgcn import Model as STGCN |
| model_backbone = STGCN() |
| else: |
| raise Exception("Undefined backbone type.") |
| return model_backbone |
